Optimal Power Flow By Newton Approach
ABSTRACT The classical optimal power flow problem with a nonseparable objective function can be solved by an explicit Newton approach. Efficient, robust solutions can be obtained for problems of any practical size or kind. Solution effort is approximately proportional to network size, and is relatively independent of the number of controls or binding inequalities. The key idea is a direct simultaneous solution for all of the unknowns in the Lagrangian function on each iteration. Each iteration minimizes a quadratic approximation of the Lagrangian. For any given set of binding constraints the process converges to the Kuhn-Tucker conditions in a few iterations. The challenge in algorithm development is to efficiently identify the binding inequalities.
SourceAvailable from: Igor Kuzle
Conference Paper: Localized market model for voltage control ancillary service[Show abstract] [Hide abstract]
ABSTRACT: In this paper provision of voltage control ancillary service through the establishment of a localized competitive reactive power market is proposed. Autonomous voltage control zones with sufficient reactive power sources are formed using electrical distance method. One reactive power market is corresponding to one voltage control zone. The proposed methodology is ensuring two kinds of payment to dynamic reactive power sources, for the availability and for the utilization. Based on the offers of reactive power producers the optimal power flow problem is solved respecting power system economic criteria and security constraints. The result of optimization algorithm is reactive power dispatch. Using of network compensation devices is maximized to ensure enough dynamic reactive power reserves in case of future system disturbances and to reduce the system operator's payment for voltage control ancillary service.3rd International Conference "The European Electricity Market. EEM-06" Challenge of the Unification, Warsaw, Poland; 05/2006
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ABSTRACT: The optimization is an important role in wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. In this paper, the real coded biogeography based optimization is proposed to minimize the operating cost with optimal setting of equality and inequality constraints of thermal power system. The proposed technique aims to improve the real coded searing ability, unravel the prematurity of solution and enhance the population assortment of the biogeography based optimization algorithm by using adaptive Gaussian mutation. This algorithm is demonstrated on the standard IEEE-30 bus system and the comparative results are made with existing population based methods.Journal of Electrical Engineering and Technology 01/2015; 10(1):56-63. DOI:10.5370/JEET.2015.10.1.056 · 0.52 Impact Factor
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ABSTRACT: The optimal power-flow problem (OPF) has always played a key role in the planning and operation of power systems. Due to the non-linear nature of the AC power-flow equations, the OPF problem is known to be non-convex, therefore hard to solve. Most proposed methods for solving the OPF rely on approximations that render the problem convex, but that consequently yield inexact solutions. Recently, Farivar and Low proposed a method that is claimed to be exact for the case of radial distribution systems, despite no apparent approximations. In our work, we show that it is, in fact, not exact. On one hand, there is a misinterpretation of the physical network model related to the ampacity constraint of the lines' current flows and, on the other hand, the proof of the exactness of the proposed relaxation requires unrealistic assumptions related to the unboundedness of specific control variables. Recently, several contributions have proposed OPF algorithms that rely on the use of the alternating-direction method of multipliers (ADMM). However, as we show in this work, there are cases for which the ADMM-based solution of the non-relaxed OPF problem fails to converge. To overcome the aforementioned limitations, we propose a specific algorithm for the solution of a non-approximated, non-convex OPF problem in radial distribution systems that is based on the method of multipliers, as well as on a primal decomposition of the OPF problem. In view of the complexity of the contribution, this work is divided in two parts. In Part I, we specifically discuss the limitations of both BFM and ADMM to solve the OPF problem. In Part II, we provide a centralized version, as well as a distributed asynchronous version of the proposed OPF algorithm and we evaluate its performances using both small-scale electrical networks, as well as a modified IEEE 13-node test feeder.